Artificially Intelligent Social Risk Adjustment

Prominent voices have called for a better way to measure, predict, and adjust for social factors in healthcare and population health. Local area characteristics are sometimes framed as a proxy for patient characteristics, but they are often independently associated with health outcomes. We have developed an “artificially intelligent” approach to risk adjustment for local social determinants of health (SDoH) using random forest models to understand life expectancy at the Census tract level. Our Local Social Inequity score draws on more than 150 neighborhood-level variables across 10 SDoH domains. As piloted in Ohio, the score explains 73 percent of the variation in life expectancy by Census tract, with a mean squared error of 4.47 years. Accurate multidimensional, cross-sector, small-area social risk scores could be useful in understanding the impact of healthcare innovations, payment models, and SDoH interventions in communities at higher risk for serious illnesses and diseases; identifying neighborhoods and areas at highest risk of poor outcomes for better targeting of interventions and resources; and accounting for factors outside of providers’ control for more fair and equitable performance/quality measurement and reimbursement.

Prominent voices have called for a better way to measure, predict, and adjust for social factors in healthcare and population health. Local area characteristics are sometimes framed as a proxy for patient characteristics, but they are often independently associated with health outcomes. We have developed an "artificially intelligent" approach to risk adjustment for local social determinants of health (SDoH) using random forest models to understand life expectancy at the Census tract level. Our Local Social Inequity score draws on more than 150 neighborhood-level variables across 10 SDoH domains. As piloted in Ohio, the score explains 73 percent of the variation in life expectancy by Census tract, with a mean squared error of 4.47 years. Accurate multidimensional, cross-sector, small-area social risk scores could be useful in understanding the impact of healthcare innovations, payment models, and SDoH interventions in communities at higher risk for serious illnesses and diseases; identifying neighborhoods and areas at highest risk of poor outcomes for better targeting of interventions and resources; and accounting for factors outside of providers' control for more fair and equitable performance/quality measurement and reimbursement.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Artificial Intelligence in Clinical Practice: How AI Technologies Impact Medical Research and Clinics compiles current research on Artificial Intelligence within medical subspecialties, helping practitioners with diagnosis, clinical decision-making, disease prediction, prevention, and the facilitation of precision medicine. The book defines the basic concepts of big data and AI in medicine and highlights current applications, challenges, ethical issues, and biases. Each chapter discusses AI applied to a specific medical subspecialty, including primary care, preventive medicine, general internal medicine, radiology, pathology, infectious disease, gastroenterology, cardiology, hematology, oncology, dermatology, ophthalmology, mental health, neurology, pulmonary, critical care, rheumatology, surgery, and OB-GYN. This is a valuable resource for clinicians, students, researchers and members of medical and biomedical fields who are interested in learning more about artificial intelligence technologies and their applications in medicine. Provides the history and overview of the various modalities of AI and their applications within each field of medicine Discusses current AI-based medical research, including landmark trials within each field of medicine Addresses the current knowledge gaps that clinicians commonly face that prevent the application of AI-based research to clinical practice Encompasses examples of specific cases and discusses challenges and biases associated with AI

This book addresses emerging issues resulting from the integration of artificial intelligence systems in our daily lives. It focuses on the cognitive, visual, social and analytical aspects of computing and intelligent technologies, highlighting ways to improve the acceptance, effectiveness, and efficiency of said technologies. Topics such as responsibility, integration and training are discussed throughout. The book also reports on the latest advances in systems engineering, with a focus on societal challenges and next-generation systems and applications for meeting them. The book is based on two AHFE 2019 Affiliated Conferences – on Artificial Intelligence and Social Computing, and on Service, Software, and Systems Engineering –, which were jointly held on July 24–28, 2019, in Washington, DC, USA.

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Proceedings of the AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2023 Hawaii Edition), Honolulu, Hawaii, USA 4-6, December 2023

This book will provide a global perspective on AI and the challenges it represents, and will focus on the digital ethics surrounding AI technology.